지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.8
- 수록면
- 228 - 238 (11page)
이용수
초록· 키워드
오류제보하기
Soil carbon is an important factor in the process of mitigating climate change and solving greenhouse gas problems. However, the previous technology for soil carbon content analysis required a lot of labor, time, and expensive equipment (i.e. an elemental analyzer). In this study, the disadvantages of previous analysis method were secured by using smartphone images and multiple regression analysis. To predict the soil carbon content, the color variables (e.g., RGB, CIE-L*a*b*, CIE-L*c*h*, and CIE-L*u*v*), gravimetric water content, and bulk density were used as statistical data. After Pearson’s correlation analysis, several variables that had high correlations were removed and then used. In addition, the result of variance inflation factor (VIF) analysis indicated that all variables should not cause multicollinearity problems. The predictive model was classified based on land use, and the predictive model for the entire sample was also derived. The adjusted coefficient of determination (Adj. R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to verify the predictive model. When the verification method was substituted for each predictive model, the reliability of the predictive model classified based on land use was high. Therefore, in order to predict the carbon content in the agricultural soil, it is efficient to assign each prediction model after classifying agricultural land.
목차
ABSTRACT
Introduction
Materials and Methods
Results and Discussion
Conclusions
References
참고문헌 (15)
참고문헌 신청
Akinwande, M.O., H.G. Dikko, and A. Samson. 2015. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 5:754-767.
Angelopoulou, T., A. Balafoutis, G. Zalidis, and D. Bochtis. 2020. From laboratory to proximal sensing spectroscopy for soil organic carbon estimation - A review. Sustainability 12:443-467.
Bradford, M.A., C.J. Carey, L. Atwood, D. Bossio, E.P. Fenichel, S. Gennet, J. Fargione, J.R.B. Fisher, E. Fuller, D.A. Kane, J. Lehmann, E.E. Oldfield, E.M. Ordway, J. Rudek, J. Sanderman, and S.A. Wood. 2019. Soil carbon science for policy and practice. Nat. Sustainability 2:1070-1072.
Fu, Y., P. Taneja, S. Lin, W. Ji, V. Adamchuk, P. Daggupati, and A. Biswas. 2020. Predicting soil organic matter from cellular phone images under varying soil moisture. Geoderma 361:114020.
Hur, S.O., Y.G. Sonn, B.K. Hyun, K.S. Shin, T.K. Oh, and J.G. Kim. 2014. Verification on PTF (Pedo-Transfer Function) estimating soil water retention based on soil properties. Korean J. Agric. Sci. 41:391-398.
Angelopoulou, T., A. Balafoutis, G. Zalidis, and D. Bochtis. 2020. From laboratory to proximal sensing spectroscopy for soil organic carbon estimation - A review. Sustainability 12:443-467.
Bradford, M.A., C.J. Carey, L. Atwood, D. Bossio, E.P. Fenichel, S. Gennet, J. Fargione, J.R.B. Fisher, E. Fuller, D.A. Kane, J. Lehmann, E.E. Oldfield, E.M. Ordway, J. Rudek, J. Sanderman, and S.A. Wood. 2019. Soil carbon science for policy and practice. Nat. Sustainability 2:1070-1072.
Fu, Y., P. Taneja, S. Lin, W. Ji, V. Adamchuk, P. Daggupati, and A. Biswas. 2020. Predicting soil organic matter from cellular phone images under varying soil moisture. Geoderma 361:114020.
Hur, S.O., Y.G. Sonn, B.K. Hyun, K.S. Shin, T.K. Oh, and J.G. Kim. 2014. Verification on PTF (Pedo-Transfer Function) estimating soil water retention based on soil properties. Korean J. Agric. Sci. 41:391-398.
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